Critical Analyses of Data Differences between FNMOC and AFGWC Spawned SSM/I Datasets

Ritchie, Adrian; Smith, Matthew; Goodman, H. Michael; Schudalla, Ronald L.; Conway, Dawn; LaFontaine, Frank J.; Moss, Don; Motta, Brian

doi:10.1175/1520-0469(1998)055<1601:caoddb>2.0.co;2

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The swath data from FNMOC have been known to include sporadic large errors in geolocation. However, Ritchie et al [1998] show that except for these cases, which affect 0.5% of the data, the geolocations compare well with independently processed data from Remote Sensing Systems. The geolocation perturbations are rare and large (4–8 scans), we use a filter on the distance between scans to flag and remove them.…”

Section: Datamentioning

confidence: 82%

Intercomparison of passive microwave sea ice concentration retrievals over the high‐concentration Arctic sea ice

et al. 2007

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[1] Measurements of sea ice concentration from the Special Sensor Microwave Imager (SSM/I) using seven different algorithms are compared to ship observations, sea ice divergence estimates from the Radarsat Geophysical Processor System, and ice and water surface type classification of 59 wide-swath synthetic aperture radar (SAR) scenes. The analysis is confined to the high-concentration Arctic sea ice, where the ice cover is near 100%. During winter the results indicate that the variability of the SSM/I concentration estimates is larger than the true variability of ice concentration. Results from a trusted subset of the SAR scenes across the central Arctic allow the separation of the ice concentration uncertainty due to emissivity variations and sensor noise from other error sources during the winter of [2003][2004]. Depending on the algorithm, error standard deviations from 2.5 to 5.0% are found with sensor noise between 1.3 and 1.8%. This is in accord with variability estimated from analysis of SSM/I time series. Algorithms, which primarily use 85 GHz information, consistently give the best agreement with both SAR ice concentrations and ship observations. Although the 85 GHz information is more sensitive to atmospheric influences, it was found that the atmospheric contribution is secondary to the influence of the surface emissivity variability. Analysis of the entire SSM/I time series shows that there are significant differences in trend between sea ice extent and area, using different algorithms. This indicates that long-term trends in surface and atmospheric properties, unrelated to sea ice concentration, influence the computed trends.

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Section: Datamentioning

confidence: 82%

Intercomparison of passive microwave sea ice concentration retrievals over the high‐concentration Arctic sea ice

et al. 2007

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“…All of the datasets analysed here derive from data sent directly from the satellites to the FNMOC and based on the results of Ritchie et al . () it is usually concluded that differences between this data stream and the same data collected and archived by AFGWC are unimportant. Further processing, and in particular inter‐satellite calibration of the SSM/I data, takes place at CSU, HOAPS, RSS and NASA (Goddard Space Science Laboratory).…”

Section: Discussionmentioning

confidence: 94%

“…Ritchie et al . () compared the antenna temperatures provided by FNMOC and RSS and found a mean difference of approximately zero and a standard deviation of 0.15 K for each channel. The comparisons presented by Ritchie et al .…”

Section: Surface Humidity Measurement and Datasetsmentioning

confidence: 99%

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A comparison of SSM/I-derived global marine surface-specific humidity datasets

et al. 2014

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Satellite-based microwave sensors have, since the 1980s, provided a means to retrieve near-surface marine specific humidity (q a ), accurate estimation of which is necessary for climate and air-sea interaction applications. Seven satellite measurement-derived monthly mean humidity datasets are compared with one another and with a dataset constructed from in situ measurements. The means, spatial and temporal structures of the datasets are shown to be markedly different, with a range of yearly, global mean q a of ∼1 g kg -1 . Comparison of the datasets derived using the same satellite measurements of brightness temperature reveals differences in q a that depend on the source of satellite data; the processing and quality control applied to the data; and the algorithm used to derive q a from the satellite measurements of brightness temperature. Regional differences between satellite-derived q a due to the choice of input data, quality control and retrieval algorithm can all exceed the accuracy requirements for surface flux calculation of ∼0.3 g kg -1 and in some cases can be several g kg -1 in monthly means for some periods and regions.

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“…A fixed cold space reflector and a reference black body hot load are used for continuous onboard calibration. (Ritchie et al, 1998), including NESDIS TDR and RSS ATT data, showed no systematic differences between these data sets. In a first step the different input data sets are preprocessed into a common internal data format containing navigated and calibrated antenna temperatures.…”

Section: The Ssm/i Instrumentmentioning

confidence: 92%

The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3

Andersson

Fennig

Klepp

et al. 2010

Earth Syst. Sci. Data

173

186

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The availability of microwave instruments on satellite platforms allows the retrieval of essential water cycle components at high quality for improved understanding and evaluation of water processes in climate modelling. HOAPS-3, the latest version of the satellite climatology "Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data" provides fields of turbulent heat fluxes, evaporation, precipitation, freshwater flux and related atmospheric variables over the global ice-free ocean. This paper describes the content, methodology and retrievals of the HOAPS climatology. A sophisticated processing chain, including all available Special Sensor Microwave Imager (SSM/I) instruments aboard the satellites of the Defense Meteorological Satellites Program (DMSP) and careful inter-sensor calibration, ensures a homogeneous time-series with dense data sampling and hence detailed information of the underlying weather situations. The completely reprocessed data set with a continuous time series from 1987 to 2005 contains neural network based algorithms for precipitation and wind speed and Advanced Very High Resolution Radiometer (AVHRR) based SST fields. Additionally, a new 85 GHz synthesis procedure for the defective SSM/I channels on DMSP F08 from 1988 on has been implemented. Freely available monthly and pentad means, twice daily composites and scan-based data make HOAPS-3 a versatile data set for studying ocean-atmosphere interaction on different temporal and spatial scales. HOAPS-3 data products are available via http://www.hoaps.org

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Critical Analyses of Data Differences between FNMOC and AFGWC Spawned SSM/I Datasets

Cited by 8 publications

References 8 publications

Intercomparison of passive microwave sea ice concentration retrievals over the high‐concentration Arctic sea ice

Intercomparison of passive microwave sea ice concentration retrievals over the high‐concentration Arctic sea ice

A comparison of SSM/I-derived global marine surface-specific humidity datasets

The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3

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